research-article

Discovering Information Propagation Patterns in Microblogging Services

Authors:

Bin GuoAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 10, Issue 1

Article No.: 7, Pages 1 - 22

https://doi.org/10.1145/2742801

Published: 22 July 2015 Publication History

Abstract

During the last decade, microblog has become an important social networking service with billions of users all over the world, acting as a novel and efficient platform for the creation and dissemination of real-time information. Modeling and revealing the information propagation patterns in microblogging services cannot only lead to more accurate understanding of user behaviors and provide insights into the underlying sociology, but also enable useful applications such as trending prediction, recommendation and filtering, spam detection and viral marketing. In this article, we aim to reveal the information propagation patterns in Sina Weibo, the biggest microblogging service in China. First, the cascade of each message is represented as a tree based on its retweeting process. Afterwards, we divide the information propagation pattern into two levels, that is, the macro level and the micro level. On one hand, the macro propagation patterns refer to general propagation modes that are extracted by grouping propagation trees based on hierarchical clustering. On the other hand, the micro propagation patterns are frequent information flow patterns that are discovered using tree-based mining techniques. Experimental results show that several interesting patterns are extracted, such as popular message propagation, artificial propagation, and typical information flows between different types of users.

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Index Terms

Discovering Information Propagation Patterns in Microblogging Services
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 10, Issue 1

July 2015

321 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2808688

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

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Copyright © 2015 ACM.

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Association for Computing Machinery

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Publication History

Published: 22 July 2015

Accepted: 01 March 2015

Revised: 01 November 2014

Received: 01 June 2014

Published in TKDD Volume 10, Issue 1

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the National Basic Research Program of China
the Specialized Research Fund for the Doctoral Program of Higher Education
the National Natural Science Foundation of China
the Program for New Century Excellent Talents in University

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Cited By

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https://dl.acm.org/doi/10.1109/TMM.2023.3305095
Yi YZhu NHe JJurcut AZhao B(2023)Toward pragmatic modeling of privacy information propagation in online social networksComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.109429219:COnline publication date: 9-Feb-2023
https://dl.acm.org/doi/10.1016/j.comnet.2022.109429
Yi YZhu NHe JJurcut AMa XLuo Y(2023)A privacy-dependent condition-based privacy-preserving information sharing scheme in online social networksComputer Communications10.1016/j.comcom.2023.01.010200:C(149-160)Online publication date: 15-Feb-2023
https://dl.acm.org/doi/10.1016/j.comcom.2023.01.010
Yang QZhu XTian YWang GZhang YChen L(2021)The influence of heterogeneity of adoption thresholds on limited information spreadingApplied Mathematics and Computation10.1016/j.amc.2021.126448411(126448)Online publication date: Dec-2021
https://doi.org/10.1016/j.amc.2021.126448
Xiao YLi WQiang SLi QXiao HLiu Y(2020)A Rumor & Anti-rumor Propagation Model Based on Data Enhancement and Evolutionary GameIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2020.3034188(1-1)Online publication date: 2020
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Zhang ZCui LFang APan ZZhang ZZhang H(2018)Information Dissemination Analysis Using a Time-Weight Null Model: A Case Study of Sina Micro-BlogIEEE Access10.1109/ACCESS.2018.28815146(71181-71193)Online publication date: 2018
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